Binggeli S, Lapierre H, Martineau R, Ouellet D R, Charbonneau E, Pellerin D
Département des sciences animales, Université Laval, Québec, QC, Canada G1V 0A6.
Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, QC, Canada, J1M 0C8.
JDS Commun. 2024 Apr 20;5(6):543-547. doi: 10.3168/jdsc.2024-0549. eCollection 2024 Nov.
A recent study assessed the ability of 4 feed evaluation models to predict milk protein yield (MPY) in a commercial context, with data of 541 cows from 23 dairy herds in the province of Québec, Canada. However, the recently published Nutrient Requirements of Dairy Cattle from the National Academies of Sciences, Engineering, and Medicine (NASEM, 2021) was not released at that time. Thus, the current study evaluated NASEM using the same dataset. To be consistent with the previous study, predicted DMI was used. Therefore, MPY was predicted using the 2 estimations of DMI proposed by NASEM: one based on animal characteristics only (DMI) and one also including ration characteristics (DMI). For each type of DMI estimates, 2 MPY predictions were made, using (1) the multivariate equation directly published in NASEM and (2) a variable efficiency of utilization of MP predicted using inputs and outputs from NASEM, published a posteriori. With the 2 approaches, multivariate and variable efficiency, the DMI yielded the best MPY predictions. The multivariate equation showed a regression bias between observed and predicted MPY with both DMI estimations. The estimated variable efficiency allowed for MPY predictions without mean and regression biases. With DMI, concordance correlation coefficients (CCC) were 0.72 and 0.78 for MPY predicted using the multivariate and variable efficiency equations, respectively. In comparison, DMI CCC were 0.60 and 0.71, respectively. In conclusion, on commercial farms, where dairy rations are usually optimized for a group of cows, estimates of DMI based on animal and rations characteristics yielded the best MPY predictions. The multivariate equation from NASEM predicted MPY with a regression bias, whereas the variable efficiency of utilization of MP based on MP and energy supplies resulted in no bias in MPY predictions.
最近的一项研究在商业背景下评估了4种饲料评估模型预测牛奶蛋白产量（MPY）的能力，数据来自加拿大魁北克省23个奶牛场的541头奶牛。然而，美国国家科学院、工程院和医学院最近发布的《奶牛营养需求》（NASEM，2021年）当时尚未发布。因此，本研究使用相同的数据集对NASEM进行了评估。为了与之前的研究保持一致，使用了预测的干物质采食量（DMI）。因此，使用NASEM提出的2种DMI估计值来预测MPY：一种仅基于动物特征（DMI），另一种还包括日粮特征（DMI）。对于每种类型的DMI估计值，使用（1）NASEM中直接公布的多变量方程和（2）使用NASEM的输入和输出事后公布的预测MP的可变利用率进行了2次MPY预测。通过多变量和可变效率这2种方法，DMI产生了最佳的MPY预测。多变量方程在2种DMI估计值下，观察到的和预测的MPY之间均显示出回归偏差。估计的可变效率使得MPY预测没有均值偏差和回归偏差。对于DMI，使用多变量和可变效率方程预测的MPY的一致性相关系数（CCC）分别为0.72和0.78。相比之下，DMI的CCC分别为0.60和0.71。总之，在商业农场中，奶牛日粮通常是针对一组奶牛进行优化的，基于动物和日粮特征的DMI估计产生了最佳的MPY预测。NASEM的多变量方程预测MPY时有回归偏差，而基于MP和能量供应的MP可变利用率导致MPY预测无偏差。
